Pairote Thongprasri. Selective harmonic elimination in single phase inverter with LC Filter using artificial neural network. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2025.
Selective harmonic elimination in single phase inverter with LC Filter using artificial neural network
Abstract:
In this paper presents the using of selective
harmonic elimination pulse width modulation (SHEPWM)
switching technique in the unipolar single phase inverter with
LC type of filter circuit and concept of using artificial neural
network (ANN) to simplify the designs of components value to
the result. The need of harmonics minimization is required in
the current inverter. The SHEPWM is one of technique that
widely used which has an advantage on selectable the harmonic
order to be eliminated and operates on lower switching
frequency scheme, leading benefit to decrease switching loss.
The low pass filter is still highly required to minimize harmonics
from SHEPWM inverter output. The LC low pass filter is
selected which is highly efficient on harmonic elimination and
improve the quality of the output. The SHEPWM and LC filter
are performed simulation in PSIM. To avoid the difficulty of
solving the SHEPWM non-linear equation and reduce the step
to find the filter components, the ANN is proposed to simplify
the process of design and simulation which provide the LC filter
value from the desired parameters by using MATALB
Simulink. The 3rd with 5th, 3rd with 7th and 3rd with 9th harmonics
order are proposed in simulation scope with fixed value of
capacitor 100 uF, 220 uF and 330uF and total harmonic
distortion (THD) from 2% to 5% to be totally 270 ANN training
data. The simulation result of 3rd and 5th elimination of
harmonic order is lower THD than other for the output before
filter and the 3rd and 9th elimination of harmonic order are
required less LC filter value than other to achieve same THD.
The parameter value from ANN model comparing with
simulation in PSIM are analyzed and result in the lowest error
at 0.27% and the highest at 1.51% from assigned testing value.
The concept of using ANN is presented which be possible
solution to apply. However, the result has such a bit of error that
indicating a greater number of training data is required to
achieve more accuracy in ANN model.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2025
Modified:
2025-06-25
Issued:
2025-06-25
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BibliograpyCitation :
In Electrical Engineering Academic Association (Thailand) and King Mongkut's University of Technology North Bangkok. Department of Electrical and Computer Engineering. 13th International Electrical Engineering Congress (iEECON 2025) (P06432). Bangkok : Electrical Engineering Academic Association (Thailand), 2025